Make Better Formulation Decisions With AI and Experimental Evidence
A promising formulation needs more than a high model score. Excipient levels, material properties and processing conditions can affect several product attributes at once, and the best next experiment is rarely the one that improves just one result.
This advanced training examines how artificial intelligence and machine learning can support formulation development for solid oral, oral liquid and injectable drug products. You will see how to organize existing experimental data, combine predictive models with design of experiments, compare candidates against product constraints and choose useful follow-up trials. Three dosage-form examples keep the discussion close to the decisions pharmaceutical scientists make in development work.
The focus is practical interpretation: what a model can learn from limited formulation data, how to recognize uncertain predictions and how to confirm a recommendation experimentally. Participants should have working knowledge of pharmaceutical formulation and experimental development. Programming experience is not required.
Why Attend?
Turn formulation records into more useful experimental datasets.
Choose follow-up trials around relevant product quality targets.
Combine predictive modeling with established experimental design.
Compare formulation options across competing performance requirements.
Recognize misleading predictions before committing laboratory resources.
Apply AI outputs with appropriate scientific and experimental checks.
Who Should Attend?
This training is particularly relevant for:
Pharmaceutical formulation and product development scientists
Solid oral and oral liquid formulation teams
Injectable formulation and parenteral development scientists
Preformulation and excipient application specialists
Pharmaceutical process development scientists
Design of experiments and quality by design specialists
Data scientists supporting pharmaceutical formulation R&D
What Makes This Training Different?
The training follows the formulation decision from product targets to experimental confirmation. Solid oral, oral liquid and injectable examples show where the same AI method needs different inputs, constraints and success measures. The injectable example concentrates on formulation composition and stability; specialized biologic, lipid nanoparticle and long-acting injectable development are outside the detailed case coverage.
Training Outline
Defining dosage-form optimization objectives
Structuring formulation and process data
Combining AI with experimental design
Solid oral dissolution and manufacturability
Oral liquid solubility and stability
Injectable composition and stability
Selecting the next informative experiments
Testing predictions and model applicability
Three practical dosage-form examples
Expert Q&A session
Turn formulation data into better candidates and more purposeful experiments. Explore AI-supported choices for tablet dissolution and manufacturability, oral liquid stability and injectable composition. Register now and take a more informed approach to the next product you develop.
